Disease burden among migrants in Morocco in 2021: A cross‑sectional study
Bibliographic record
Abstract
BACKGROUND: Morocco, traditionally an emigration country, has evolved into not only a transit country to Europe but also a country of residence for an increasing number of migrants, with 102,400 migrants in 2019. This is due to its geographic location, the induced effects of its "African policy," and the various laws adopted by Moroccan legislators in recent years. The purpose of this study is to determine the prevalence of communicable and noncommunicable diseases among migrants such as Hepatitis C virus (HCV), human immunodeficiency virus (HIV), diabetes, and hypertension. METHODS: We conducted a cross-sectional study in Oujda, Morocco, between November and December 2021. Face-to-face interviews with enrolled migrants aged 18 years and over, present in Oujda and attending an association, were carried out to collect socio-demographic data, lifestyle behaviors, and clinical parameters. Diabetes and hypertension were the primary outcomes. The Pearson's chi-squared test and the student's t-test were used to assess the bivariate associations between primary outcomes and categorical and continuous variables. In a multivariate model, we adjusted for predictors that were significant (p-value ≤0.05) in bivariate analysis to estimate Adjusted Odd Ratios (AOR) and 95% confidence intervals (CI). RESULTS: There were 495 migrants enrolled, with a male/female ratio of two and an average age of 27.3±11.5 years (mean±standard deviation), ranging from 18 to 76 years. Hepatitis C virus, human immunodeficiency virus, diabetes, and hypertension were found in 1%, 0.2%, 3.8%, and 27.7% of the population, respectively. Family history of diabetes was a risk factor for diabetes in the Oujda migrant population, with an Adjusted Odds Ratio (AOR) of 5.36; CI% [1.23-23.28]. Age (AOR of 1.1; CI% [1.06-1.13]) and African origin (AOR of 3.07; CI% [1.06-8.92]) were identified as risk factors for hypertension. CONCLUSION: Migrants in Oujda are healthy. The high prevalence of hypertension, as well as the presence of HCV and HIV positive cases, emphasizes the importance of routine screening for hypertension, HCV, and HIV in order to detect and treat these diseases as early as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".